I wrote a post a couple of months ago about the drawbacks of accessing multiple data sources. I discussed how it was essential for project managers to have the available technology, designed for usability that enables them to have the necessary visibility of their clinical trial activity. Now I would like to explore the kind of electronic data capture (EDC) system that benefits from technology advancements and evolving standards. What does it take to create an effective modern EDC system?
First, to provide context, it is important to understand where the market is heading. We know that EDC has become a commodity. However, as we move into the mobile digital age, multiple current and future trends need to be considered when selecting an EDC application. For example, the combination of social media, smartphone availability, mobile health (mHealth) and bring your own device (BYOD) for electronic clinical outcome assessment (eCOA) suddenly make remote and real-world studies a possibility. There is a move towards patient-centric trials.
This, coupled with the evolution of eSource applications providing a novel solution to data transcription redundancies and errors, highlights the importance of incorporating these trends in order to be future- proof. What’s clear is that EDC solution development needs to evolve as clinical development adapts to the mobile digital age (you can read more about this by downloading this free Emerging Trends in EDC whitepaper).
For data management teams, the predictors that influence whether they can ensure a study is completed on time can be affected by these factors:
Having quick access to clinical trial data and metrics
Providing a flexible solution that easily adapts to their clients’ systems
Providing quick study builds and minimization of downtime during mid-study changes
If any of these factors are not met, it can cause additional delays and costs to completion of the study. The challenge is that sponsors and contract research organizations (CROs) want a solution that is both affordable and offers this level of flexibility. This can be accomplished by standard integration between different applications. However, this does come with some caveats.
As mentioned in my earlier post, one of the main issues with multiple integration layers between different applications is that it can make extracting the correct data difficult and time-consuming; this can have an impact on making informed timely decisions about a trial as data can be harder to find and may suffer from validation issues. It also makes it more difficult to build studies across different data management applications. Combine this with the fact that not all systems are 100% compatible, and you are looking at complexity fatigue, which tends to be a drain on resources.
To hear more from a customer that has benefited from such a system, watch this on-demand webinar or read their story here.